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All Outputs (30)

Defending against adversarial machine learning attacks using hierarchical learning: A case study on network traffic attack classification (2022)
Journal Article

Machine learning is key for automated detection of malicious network activity to ensure that computer networks and organizations are protected against cyber security attacks. Recently, there has been growing interest in the domain of adversarial mach... Read More about Defending against adversarial machine learning attacks using hierarchical learning: A case study on network traffic attack classification.

Functionality-preserving adversarial machine learning for robust classification in cybersecurity and intrusion detection domains: A survey (2022)
Journal Article

Machine learning has become widely adopted as a strategy for dealing with a variety of cybersecurity issues, ranging from insider threat detection to intrusion and malware detection. However, by their very nature, machine learning systems can introdu... Read More about Functionality-preserving adversarial machine learning for robust classification in cybersecurity and intrusion detection domains: A survey.

Feature vulnerability and robustness assessment against adversarial machine learning attacks (2021)
Presentation / Conference Contribution

Whilst machine learning has been widely adopted for various domains, it is important to consider how such techniques may be susceptible to malicious users through adversarial attacks. Given a trained classifier, a malicious attack may attempt to craf... Read More about Feature vulnerability and robustness assessment against adversarial machine learning attacks.

Permissions snapshots: Assessing users' adaptation to the Android runtime permission model (2017)
Presentation / Conference Contribution

© 2016 IEEE. The Android operating system changed its security-and privacy-related permission model recently, offering its users the ability to control resources that applications are allowed to access on their devices. This major change to the tradi... Read More about Permissions snapshots: Assessing users' adaptation to the Android runtime permission model.

A comparative study of android users’ privacy preferences under the runtime permission model (2017)
Presentation / Conference Contribution

© Springer International Publishing AG 2017. Android users recently were given the ability to selectively grant access to sensitive resources of their mobile devices when apps request them at runtime. The Android fine-grained runtime permission model... Read More about A comparative study of android users’ privacy preferences under the runtime permission model.

Mass surveillance in cyberspace and the lost art of keeping a secret: Policy lessons for government after the snowden leaks (2016)
Presentation / Conference Contribution

© Springer International Publishing Switzerland 2016. Global security concerns, acts of terrorism and organised crime activity have motivated nation states to delve into implementing measures of mass surveillance in cyberspace, the breadth of which w... Read More about Mass surveillance in cyberspace and the lost art of keeping a secret: Policy lessons for government after the snowden leaks.